Answer:
<h2>1. x = 4</h2><h2>2. x = 20</h2>
Step-by-step explanation:
1.
ΔABC and ΔAJK are similar (AA). Therefore the sides are in proportion:
We have:
AC = 1 + 4 = 5
AJ = 1
AB = 1 + x
AK = 1
Substitute:
<em>subtract 1 from both sides</em>
2.
ΔVUT and ΔVMN are similar (AA). Therefore the sides are in proportion:
We hve:
VU = x + 8
VM = x
VT = 49
VN = 49 - 14 = 35
Substitute:
<em>cross multiply</em>
<em>use the distributive property a(c + b) = ab + ac</em>
<em>subtract 35x from both sides</em>
<em>divide both sides by 14</em>
Answer:
P[X=3,Y=3] = 0.0416
Step-by-step explanation:
Solution:
- X is the RV denoting the no. of customers in line.
- Y is the sum of Customers C.
- Where no. of Customers C's to be summed is equal to the X value.
- Since both events are independent we have:
P[X=3,Y=3] = P[X=3]*P[Y=3/X=3]
P[X=3].P[Y=3/X=3] = P[X=3]*P[C1+C2+C3=3/X=3]
P[X=3]*P[C1+C2+C3=3/X=3] = P[X=3]*P[C1=1,C2=1,C3=1]
P[X=3]*P[C1=1,C2=1,C3=1] = P[X=3]*(P[C=1]^3)
- Thus, we have:
P[X=3,Y=3] = P[X=3]*(P[C=1]^3) = 0.25*(0.55)^3
P[X=3,Y=3] = 0.0416
Answer:
Yes, because -43 can be turned into a fraction. Ex. -43/1.
There could be a strong correlation between the proximity of the holiday season and the number of people who buy in the shopping centers.
It is known that when there are vacations people tend to frequent shopping centers more often than when they are busy with work or school.
Therefore, the proximity in the holiday season is related to the increase in the number of people who buy in the shopping centers.
This means that there is a strong correlation between both variables, since when one increases the other also does. This type of correlation is called positive. When, on the contrary, the increase of one variable causes the decrease of another variable, it is said that there is a negative correlation.
There are several coefficients that measure the degree of correlation (strong or weak), adapted to the nature of the data. The best known is the 'r' coefficient of Pearson correlation
A correlation is strong when the change in a variable x produces a significant change in a variable 'y'. In this case, the correlation coefficient r approaches | 1 |.
When the correlation between two variables is weak, the change of one causes a very slight and difficult to perceive change in the other variable. In this case, the correlation coefficient approaches zero
